Drug related deaths in the United States of America

Drug related deaths in the US - A case study

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Overview and Motivation

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Summary of study

Let´s jump right in an take a look at the total number of deaths by drugs scaled for 10 000 inhabitants per state.

We scale the data so the relationship between the number of deaths become equal for the number of inhabitants in every state. Then we divide all of the states into four regions:

Northeast

Midwest

South

West

Deaths by drugs for all states compared to one another in 2016:

How did they rank in 2017?

State	Deaths per 10 000 inhabitant	Rank
District of Columbia	67.584604	1
West Virginia	63.899986	2
Ohio	52.540842	3
Pennsylvania	50.511744	4
Maryland	45.563919	5
Kentucky	42.629676	6
Delaware	41.814790	7
New Hampshire	41.138074	8
Massachusetts	37.697788	9
Rhode Island	37.616585	10
Connecticut	35.098534	11
Maine	33.640066	12
Florida	33.470295	13
Tennessee	31.959776	14
Indiana	31.790156	15
New Jersey	31.742316	16
Michigan	31.503831	17
Nevada	29.179073	18
New Mexico	28.907077	19
Missouri	27.873136	20
North Carolina	26.844881	21
Louisiana	26.687018	22
Vermont	26.184265	23
Oklahoma	25.624791	24
Arizona	25.016141	25
South Carolina	24.457369	26
Utah	24.263073	27
Illinois	24.085293	28
Wisconsin	23.718754	29
Colorado	21.483269	30
Alaska	21.235613	31
Virginia	20.664846	32
Alabama	19.759101	33
Washington	17.855062	34
Georgia	17.736893	35
Idaho	16.348825	36
Hawaii	16.195716	37
Arkansas	16.135175	38
Minnesota	15.290441	39
Wyoming	15.034998	40
Oregon	14.963396	41
California	14.773633	42
New York	14.671981	43
Montana	13.945833	44
North Dakota	13.132557	45
Mississippi	13.078333	46
Kansas	13.030601	47
Texas	12.954382	48
Iowa	12.888233	49
South Dakota	10.545914	50
Nebraska	8.091983	51

What is happening in district of columbia???

This is some serious numbers. The total average drug deaths per state for 2015 is 11 585 and worse, it increased to 15 856 in 2017. This is an increase of 36.87% from 2015 to 2017, implefying that it is a serious problem in the US. The total number of deaths by drugs was 590 825 in 2015, 682 084 in 2016 and 808 661 in 2017. This equals a total of 2 081 570 people just for the three years this case study is studying. That´s the same as the total population of Slovenia to put things in perspective. Wiped out over three years.

But how many drug deaths do we find compared to all deaths?

A small percentage: 1.9 % of all deaths in average in the US was drug related in 2015 2.2 % of all deaths in average in the US was drug related in 2016 2.45 % of all deaths in average in the US was drug related in 2017

Low income and high unemployment will result in high overdose rate?

2016 numbers

Our assupmption is that low income and high unemployment equals high od-rate. So we take a look at the top 10 states of high od-rate, lowest income and highest unemployment.

State	Deaths pr 10000 inhabitants	Rank
West Virginia	53	1
New Hampshire	39	2
Ohio	39	3
District of Columbia	38	4
Rhode Island	38	5
Kentucky	36	6
Pennsylvania	36	7
Massachusetts	35	8
Maryland	34	9
Connecticut	30	10

State	Median household income 2016	Rank
Mississippi	40528	51
Arkansas	42336	50
West Virginia	42644	49
Alabama	44758	48
Kentucky	44811	47
Louisiana	45652	46
New Mexico	45674	45
Tennessee	46574	44
South Carolina	46898	43
Oklahoma	48038	42

State	The unemployment rate in percent of states labor force 2016	Rank
Alaska	6.9	51
New Mexico	6.7	50
District of Columbia	6.1	48
West Virginia	6.1	48
Louisiana	6.0	47
Alabama	5.9	46
Illinois	5.8	44
Mississippi	5.8	44
Nevada	5.7	43
California	5.5	42

As we can se this is not allways the case. West Wirginia stands out and is represented badly in all three categories. The people working in DC make good money, but both unemployment and od-rate are high. Kentucky is represented in the table for low income. Maryland is the states with highest income the last couple of years by a solid margin, and is the 9th worst place for od in the country. Othervise the similarities was not as strong as expected.

But high income and low unemployment would equal low od-rate right? Not for Maryland thats for sure.

State	Deaths pr 10000 inhabitants	Rank
Nebraska	7.5	1
South Dakota	9.3	2
North Dakota	10.9	3
Texas	11.7	4
Iowa	11.9	5
New York	12.6	6
Kansas	13.0	7
Mississippi	13.2	8
Minnesota	13.9	9
Montana	14.2	10

State	Median household income 2016	Rank
Maryland	76067	1
Alaska	74444	2
New Jersey	73702	3
District of Columbia	72935	4
Hawaii	71977	5
Connecticut	71755	6
Massachusetts	70954	7
New Hampshire	68485	8
Virginia	66149	9
California	63783	10

State	The unemployment rate in percent of states labor force 2016	Rank
New Hampshire	2.9	1
Hawaii	2.9	1
South Dakota	3.0	3
North Dakota	3.1	4
Nebraska	3.1	4
Vermont	3.2	6
Colorado	3.3	7
Utah	3.4	8
Iowa	3.6	9
Maine	3.8	10

What about the states with High(good) and low(bad) temperatures?

State	TempC	Rank
Florida	22	1
Hawaii	21	2
Louisiana	19	3
Texas	18	4
Georgia	18	5
Mississippi	17	6
Alabama	17	7
South Carolina	17	8
Arkansas	16	9
Arizona	16	10

State	TempC	Rank
Alaska	-3.0	1
North Dakota	4.7	2
Maine	5.0	3
Minnesota	5.1	4
Wyoming	5.6	5
Montana	5.9	6
Vermont	6.1	7
Wisconsin	6.2	8
New Hampshire	6.6	9
Michigan	6.9	10

blablabla

## [1] 0.87

## [1] 0.25

## [1] 0.012

## [1] 0.32

Remember that number of incidents do not equal number of death, since people could be affected by several drugs and all of them would be registerd.

What will a linear regression model say about OD dependent on income, weather and unemployment?

## 
## Call:
## lm(formula = OD16_relation ~ Median_income16 + PrecipitationMM + 
##     Clear_days + Rate2016, data = comparedata)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.001323 -0.000648 -0.000169  0.000505  0.002511 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)      3.35e-04   1.45e-03    0.23    0.818  
## Median_income16  1.02e-08   1.48e-08    0.69    0.496  
## PrecipitationMM  4.60e-07   3.84e-07    1.20    0.238  
## Clear_days      -3.18e-06   5.20e-06   -0.61    0.544  
## Rate2016         2.68e-04   1.34e-04    2.01    0.051 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9e-04 on 45 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.145,  Adjusted R-squared:  0.0687 
## F-statistic:  1.9 on 4 and 45 DF,  p-value: 0.126

Interactive map with summary of every state

Heatmap of drug ratio US

The BIG DATA

Since this is a case study, it would be unreasenable of us not to show you the data we have been working on as a part of this task. The data is a result of scraping, gathering, massaging and arranging multiple data sets with tens of thousand observations. This is the end result the task has been created with. See for yourself, and feel free to do your own calculations. The code for everything is on our github. Thanks for reading. - Ørjan, Preben and Daniel.